To improve the robustness of the model's prediction and the OOD detection performance we propose In-Distribution Voting (IDV): a sample is classified as ID if at least one class-wise prediction is above the class-wise ID threshold, as illustrated in Figure REF .
As the ID threshold is determined using ID and OOD data, we can expect to have some OOD data at hand and leverage it to break the “closed world” assumption during training, i.e. force the model not to condition the prediction on the chest X-ray input assumption.
To do so we adapt approaches proposed in the literature [1]}, [2]} and include OOD data into the training data set, so called outlier exposure [1]} or negative data [4]}.
Note, while “no finding” samples have no labeled diseases, we consider them as ID, as they are chest X-rays.
The ID thresholds are set for each class independently to have 95 % sensitivity on the validation data set (containing ID and OOD images).
